Comparer des méthodes

	Gaussian Process en ligne	Descente de gradient stochastique (SGD)
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2002	1951
Auteur d'origine	Csató, L. & Opper, M.	Robbins, H. & Monro, S.
Type	Bayesian nonparametric model (sequential/online)	First-order iterative optimization algorithm
Source fondatrice	Csató, L. & Opper, M. (2002). Sparse on-line Gaussian processes. Neural Computation, 14(3), 641–668.	Robbins, H. & Monro, S. (1951). A Stochastic Approximation Method. The Annals of Mathematical Statistics, 22(3), 400–407.
Alias	OGP, sparse online GP, sequential Gaussian process, incremental Gaussian process	SGD, online gradient descent, incremental gradient descent, mini-batch gradient descent
Apparentées	3	3
Résumé	Online Gaussian Process (OGP) extends the Bayesian nonparametric GP framework to streaming or sequentially arriving data. Instead of recomputing the full GP posterior from scratch as each observation arrives, OGP maintains a compact summary — a sparse set of inducing points — and updates it incrementally, making probabilistic regression and classification feasible in real-time and large-scale settings.	Stochastic Gradient Descent (SGD) is a first-order iterative optimization algorithm, rooted in the stochastic approximation framework introduced by Robbins and Monro in 1951, that minimizes an objective function by updating model parameters using the gradient computed on a single randomly selected training example (or a small mini-batch) at each step. It is the core optimization engine behind modern machine learning and deep learning, enabling the training of models on datasets too large to fit in memory.